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Selection and Placement of Sensors for Electric Motors: A Review and Preliminary Investigation

Author

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  • Mathew Habyarimana

    (Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4001, South Africa
    These authors contributed equally to this work.)

  • Abayomi A. Adebiyi

    (Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4001, South Africa
    These authors contributed equally to this work.)

Abstract

This review explores sensor selection and placement strategies for electric motor monitoring in industrial settings. A wide range of sensor types including temperature, vibration, current, and position sensors—are evaluated in terms of their technical features and application constraints. Preliminary experimental data on vibration sensors highlight how signal amplitude varies with sensor placement, reinforcing the importance of correct positioning. However, this study stops short of applying AI/ML techniques to optimize placement. Accordingly, this paper serves as a foundational step toward developing intelligent sensor deployment frameworks. Future work will build on this review by integrating supervised learning, dimensionality reduction, and reinforcement learning techniques to automate sensor placement and improve condition monitoring in electric motors.

Suggested Citation

  • Mathew Habyarimana & Abayomi A. Adebiyi, 2025. "Selection and Placement of Sensors for Electric Motors: A Review and Preliminary Investigation," Energies, MDPI, vol. 18(13), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3484-:d:1692774
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    References listed on IDEAS

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    1. Ganesh Kumar Balakrishnan & Chong Tak Yaw & Siaw Paw Koh & Tarek Abedin & Avinash Ashwin Raj & Sieh Kiong Tiong & Chai Phing Chen, 2022. "A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations," Energies, MDPI, vol. 15(16), pages 1-37, August.
    2. Pieter Nguyen Phuc & Hendrik Vansompel & Dimitar Bozalakov & Kurt Stockman & Guillaume Crevecoeur, 2019. "Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model," Energies, MDPI, vol. 13(1), pages 1-27, December.
    3. Mathew Habyarimana & Abayomi A. Adebiyi, 2025. "A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines," Energies, MDPI, vol. 18(7), pages 1-21, March.
    4. Anyu Cheng & Yi Xin & Hang Wu & Lixin Yang & Banghuai Deng, 2023. "A Review of Sensor Applications in Electric Vehicle Thermal Management Systems," Energies, MDPI, vol. 16(13), pages 1-29, July.
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